Compressed sensing example

1. Where is compressed sensing used?
2. What is compressed sensing used for?
3. What is compressed sensing in image processing?
4. What is compressive sensing theory?

Where is compressed sensing used?

In a wide range of applications, the compressive sensing can be applied, like data mining [6,7], text processing [8], signal processing [9,10], agriculture on-board data processing [11,12], image enhancement [13,14], acoustic OFDM [15], medical image processing [16–18], image adaptation [19] Electrocardiogram ...

What is compressed sensing used for?

Compressed sensing can be used to improve image reconstruction in holography by increasing the number of voxels one can infer from a single hologram. It is also used for image retrieval from undersampled measurements in optical and millimeter-wave holography.

What is compressed sensing in image processing?

Compressed sensing (CS) is an image acquisition method, where only few random measurements are taken instead of taking all the necessary samples as suggested by Nyquist sampling theorem. It is one of the most active research areas in the past decade.

What is compressive sensing theory?

The compressive sensing theory states that the signal can be reconstructed using just a small set of randomly acquired samples if it has a sparse (concise) representation in certain transform domain.